Health conditions that impact fitness-to-practice in physicians: a scoping review
Bibliographic record
Abstract
BACKGROUND: It has been over 50 years since health conditions in physicians were first suggested to affect their fitness-to-practice, with consequent impacts on patient safety and patient care. Recent policy positions from physician regulatory bodies express a desire for clarity regarding the impact of these health conditions alongside their standardization in physician regulatory processes. Furthermore, these conditions have not been fully enumerated. Therefore, this scoping review intended to find all health conditions which were identified in the literature to impact physician fitness-to-practice. METHODS: A specialist librarian developed and executed a systematic literature search in Ovid MEDLINE, Embase via Ovid, APA PsycINFO, and ProQuest Dissertations & Theses Global (to January 2024). The SPIDER framework was used for inclusion criteria and records were screened independently by two reviewers by title and abstract, and then by full text. Any study addressing a health condition identified as able to affect fitness-to-practice in physicians and surgeons, physician assistants, or medical trainees was eligible. RESULTS: In 403 eligible records of 2542 screened, 4336 total mentions of 203 fitness-to-practice-related health conditions were identified. Conditions relating to mental health issues (32.0%) and drug/substance use (26.0%) comprised more than half of the condition reports. This was followed by neurological conditions (13.2%), medical conditions (12.8%), alcohol use (6.0%), addiction (3.0%), and aging (2.9%) as well as conditions affecting dexterity/fine motor skills/psychomotor performance (2.1%), vision (1.4%), and hearing (0.6%). CONCLUSIONS: This scoping review identified a wide variety of health conditions which could affect physician fitness-to-practice, with a potential impact on patient care and safety. These conditions have persisted in the literature, and we commend them to the attention of practicing physicians, researchers, regulators, and physician health programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".